IEEE Communications Society Seminar – A Probabilistic Theory of Deep Learning – May 18, 2017

Event Title: Technical Seminar – A Probabilistic Theory of Deep Learning
Speaker: Dr. Richard Baraniuk
Date: Thursday May 18, 2017
Time: 2:00 pm
Location: Room E3-262 , EITC, University of Manitoba, Fort Garry Campus
Abstract: A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, a principled route to their improvement, and new avenues for exploration.
Biography of the Speaker: Richard G. Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering at Rice University.  He received the B.Sc. degree in 1987 from the University of Manitoba, the M.Sc. degree in 1988 from the University of Wisconsin-Madison, and the Ph.D. degree in 1992 from the University of Illinois at Urbana-Champaign, all in Electrical Engineering.  His research interests lie in  new theory, algorithms, and hardware for sensing, signal processing, and machine learning.  He is a Fellow of the American Academy of Arts and Sciences, National Academy of Inventors, American Association for the Advancement of Science, and IEEE.  He has received the DOD Vannevar Bush Faculty Fellow Award (National Security Science and Engineering Faculty Fellow), the IEEE Signal Processing Society Technical Achievement Award, and the IEEE James H. Mulligan, Jr. Education Medal.  He holds 28 US and 4 foreign patents that have been licensed to 2 companies.
Other information: The seminar is free and open to all who wish to attend. For more information please contact Dr. Jun Cai.

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